Towards an Intelligent Intrusion Detection System to Detect Malicious Activities in Cloud Computing
Several sectors have embraced Cloud Computing (CC) due to its inherent characteristics, such as scalability and flexibility. However, despite these advantages, security concerns remain a significant challenge for cloud providers. CC introduces new vulnerabilities, including unauthorized access, data...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/17/9588 |
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| author | Hanaa Attou Mouaad Mohy-eddine Azidine Guezzaz Said Benkirane Mourade Azrour Abdulatif Alabdultif Naif Almusallam |
| author_facet | Hanaa Attou Mouaad Mohy-eddine Azidine Guezzaz Said Benkirane Mourade Azrour Abdulatif Alabdultif Naif Almusallam |
| author_sort | Hanaa Attou |
| collection | DOAJ |
| description | Several sectors have embraced Cloud Computing (CC) due to its inherent characteristics, such as scalability and flexibility. However, despite these advantages, security concerns remain a significant challenge for cloud providers. CC introduces new vulnerabilities, including unauthorized access, data breaches, and insider threats. The shared infrastructure of cloud systems makes them attractive targets for attackers. The integration of robust security mechanisms becomes crucial to address these security challenges. One such mechanism is an Intrusion Detection System (IDS), which is fundamental in safeguarding networks and cloud environments. An IDS monitors network traffic and system activities. In recent years, researchers have explored the use of Machine Learning (ML) and Deep Learning (DL) approaches to enhance the performance of IDS. ML and DL algorithms have demonstrated their ability to analyze large volumes of data and make accurate predictions. By leveraging these techniques, IDSs can adapt to evolving threats, detect previous attacks, and reduce false positives. This article proposes a novel IDS model based on DL algorithms like the Radial Basis Function Neural Network (RBFNN) and Random Forest (RF). The RF classifier is used for feature selection, and the RBFNN algorithm is used to detect intrusion in CC environments. Moreover, the datasets Bot-IoT and NSL-KDD have been utilized to validate our suggested approach. To evaluate the impact of our approach on an imbalanced dataset, we relied on Matthew’s Correlation Coefficient (MCC) as a normalized measure. Our method achieves accuracy (ACC) higher than 92% using the minimum features, and we managed to increase the MCC from 28% to 93%. The contributions of this study are twofold. Firstly, it presents a novel IDS model that leverages DL algorithms, demonstrating an improved ACC higher than 92% using minimal features and a substantial increase in MCC from 28% to 93%. Secondly, it addresses the security challenges specific to CC environments, offering a promising solution to enhance security in cloud systems. By integrating the proposed IDS model into cloud environments, cloud providers can benefit from enhanced security measures, effectively mitigating unauthorized access and potential data breaches. The utilization of DL algorithms, RBFNN, and RF has shown remarkable potential in detecting intrusions and strengthening the overall security posture of CC. |
| first_indexed | 2024-03-10T23:28:43Z |
| format | Article |
| id | doaj.art-77a3bc5e4e414bd287b6f74392cb5403 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:28:43Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-77a3bc5e4e414bd287b6f74392cb54032023-11-19T07:49:06ZengMDPI AGApplied Sciences2076-34172023-08-011317958810.3390/app13179588Towards an Intelligent Intrusion Detection System to Detect Malicious Activities in Cloud ComputingHanaa Attou0Mouaad Mohy-eddine1Azidine Guezzaz2Said Benkirane3Mourade Azrour4Abdulatif Alabdultif5Naif Almusallam6Technology Higher School Essaouira, Cadi Ayyad University, Essaouira 44000, MoroccoTechnology Higher School Essaouira, Cadi Ayyad University, Essaouira 44000, MoroccoTechnology Higher School Essaouira, Cadi Ayyad University, Essaouira 44000, MoroccoTechnology Higher School Essaouira, Cadi Ayyad University, Essaouira 44000, MoroccoInformatique Décisionnelle et Modélisation des Systèmes (IDMS) Team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Errachidia 52000, MoroccoDepartment of Computer Science, College of Computer, Qassim University, Buraydah 52571, Saudi ArabiaDepartment of Management Information Systems (MIS), College of Business Administration, King Faisal University (KFU), Al-Ahsa 31982, Saudi ArabiaSeveral sectors have embraced Cloud Computing (CC) due to its inherent characteristics, such as scalability and flexibility. However, despite these advantages, security concerns remain a significant challenge for cloud providers. CC introduces new vulnerabilities, including unauthorized access, data breaches, and insider threats. The shared infrastructure of cloud systems makes them attractive targets for attackers. The integration of robust security mechanisms becomes crucial to address these security challenges. One such mechanism is an Intrusion Detection System (IDS), which is fundamental in safeguarding networks and cloud environments. An IDS monitors network traffic and system activities. In recent years, researchers have explored the use of Machine Learning (ML) and Deep Learning (DL) approaches to enhance the performance of IDS. ML and DL algorithms have demonstrated their ability to analyze large volumes of data and make accurate predictions. By leveraging these techniques, IDSs can adapt to evolving threats, detect previous attacks, and reduce false positives. This article proposes a novel IDS model based on DL algorithms like the Radial Basis Function Neural Network (RBFNN) and Random Forest (RF). The RF classifier is used for feature selection, and the RBFNN algorithm is used to detect intrusion in CC environments. Moreover, the datasets Bot-IoT and NSL-KDD have been utilized to validate our suggested approach. To evaluate the impact of our approach on an imbalanced dataset, we relied on Matthew’s Correlation Coefficient (MCC) as a normalized measure. Our method achieves accuracy (ACC) higher than 92% using the minimum features, and we managed to increase the MCC from 28% to 93%. The contributions of this study are twofold. Firstly, it presents a novel IDS model that leverages DL algorithms, demonstrating an improved ACC higher than 92% using minimal features and a substantial increase in MCC from 28% to 93%. Secondly, it addresses the security challenges specific to CC environments, offering a promising solution to enhance security in cloud systems. By integrating the proposed IDS model into cloud environments, cloud providers can benefit from enhanced security measures, effectively mitigating unauthorized access and potential data breaches. The utilization of DL algorithms, RBFNN, and RF has shown remarkable potential in detecting intrusions and strengthening the overall security posture of CC.https://www.mdpi.com/2076-3417/13/17/9588cloud securityanomaly detectionfeatures engineeringradial basis function neural networkrandom forest |
| spellingShingle | Hanaa Attou Mouaad Mohy-eddine Azidine Guezzaz Said Benkirane Mourade Azrour Abdulatif Alabdultif Naif Almusallam Towards an Intelligent Intrusion Detection System to Detect Malicious Activities in Cloud Computing Applied Sciences cloud security anomaly detection features engineering radial basis function neural network random forest |
| title | Towards an Intelligent Intrusion Detection System to Detect Malicious Activities in Cloud Computing |
| title_full | Towards an Intelligent Intrusion Detection System to Detect Malicious Activities in Cloud Computing |
| title_fullStr | Towards an Intelligent Intrusion Detection System to Detect Malicious Activities in Cloud Computing |
| title_full_unstemmed | Towards an Intelligent Intrusion Detection System to Detect Malicious Activities in Cloud Computing |
| title_short | Towards an Intelligent Intrusion Detection System to Detect Malicious Activities in Cloud Computing |
| title_sort | towards an intelligent intrusion detection system to detect malicious activities in cloud computing |
| topic | cloud security anomaly detection features engineering radial basis function neural network random forest |
| url | https://www.mdpi.com/2076-3417/13/17/9588 |
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